Flexibility Resource Planning and Stability Optimization Methods for Power Systems with High Penetration of Renewable Energy

Haiteng Han (), Xiangchen Jiang, Yang Cao (), Xuanyao Luo, Sheng Liu and Bei Yang
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Haiteng Han: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Xiangchen Jiang: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Yang Cao: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China
Xuanyao Luo: Power China Jiangxi Electric Power Engineering CO., LTD., Nanchang 330006, China
Sheng Liu: Power China Jiangxi Electric Power Engineering CO., LTD., Nanchang 330006, China
Bei Yang: School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China

Energies, 2025, vol. 18, issue 15, 1-33

Abstract: With the accelerating global transition toward sustainable energy systems, power grids with a high share of renewable energy face increasing challenges due to volatility and uncertainty, necessitating advanced flexibility resource planning and stability optimization strategies. This paper presents a comprehensive distribution network planning framework that coordinates and integrates multiple types of flexibility resources through joint optimization and network reconfiguration to enhance system adaptability and operational resilience. A novel virtual network coupling modeling approach is proposed to address topological constraints during network reconfiguration, ensuring radial operation while allowing rapid topology adjustments to isolate faults and restore power supply. Furthermore, to mitigate the uncertainty and fault risks associated with extreme weather events, a CVaR-based risk quantification framework is incorporated into a bi-level optimization model, effectively balancing investment costs and operational risks under uncertainty. In this model, the upper-level planning stage optimizes the siting and sizing of flexibility resources, while the lower-level operational stage coordinates real-time dispatch strategies through demand response, energy storage operation, and dynamic network reconfiguration. Finally, a hybrid SA-PSO algorithm combined with conic programming is employed to enhance computational efficiency while ensuring high solution quality for practical system scales. Case study analyses demonstrate that, compared to single-resource configurations, the proposed coordinated planning of multiple flexibility resources can significantly reduce the total system cost and markedly improve system resilience under fault conditions.

Keywords: flexibility resource planning; distribution network reconfiguration; conditional value-at-risk; multi-energy coordination optimization; distributed energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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